This is further complicated by the fact that AI is moving fast. Trillions of dollars are being spent globally on new initiatives, and the competitive landscape is being turned on its head. Every quarter, the list of worthy investments grows longer, and every leader I speak with can make a compelling case for why their initiative matters most.

Here’s what hasn’t changed: capital is finite. Yes, you could raise more money, but there is no inexhaustible pot of gold waiting to be given out. If money is going to one area, you’re making a trade-off and spending less somewhere else.

At ServiceNow, that is not a theoretical exercise. We recently completed our $7.75 billion acquisition of Armis — one of the biggest capital allocation decisions in our history, and a bet that closing the gap between asset visibility and cyber risk mattered more right now than half a dozen other initiatives competing for the same dollars. These are decisions about where we believe enterprise AI is going, what capabilities we need to own, and how much conviction we have before the ROI is obvious to everyone.

As President and CFO, I sit at the intersection of growth and financial discipline. It is my job to make deliberate calls about where to invest, when to wait, and when to say no — and, like many other enterprise leaders right now, I’m aiming at a moving target.

Here are the questions I believe every major investment decision must answer.

1. Does it deepen our competitive moat?

I stress-test every investment decision against a simple question: does it strengthen what is hardest to copy about our business?

Right now, that question carries more weight than ever. When intelligence is cheap and AI can produce functional code in minutes, a meaningful feature advantage can be matched by your competitor in weeks. That raises the bar for what is actually worth funding.

Investment must now balance strategic parity — ensuring you aren’t left behind — with the differentiation required to be a market leader. Increasingly, one path to achieving this is pairing AI with proprietary data, hard-won expertise, and systems built over years.

Take JPMorgan Chase, which built its LLM Suite platform in-house and connected it to the firm’s own data and systems, creating a unified and unique AI resource that others can’t easily duplicate. At ServiceNow, we’re building on a different set of advantages: 20+ years of helping customers execute more than 100 billion workflows, which has given us deep domain expertise, proprietary data, and a massive install base of customers embedded broadly and deeply across our platform.

For every company, the moat will look different. The point is to be honest about the aspects of your business that are genuinely hard to replicate, and to invest in whatever compounds that advantage.

It also means being practical about the path you take to get there. We pride ourselves on being an organic growth and innovation machine. In a market moving this quickly, though, even organizations with a strong build-it-ourselves culture must be open to inorganic plays that bring in critical capabilities and talent faster than they can be developed internally. For many companies, it’s one of the harder shifts this moment requires.

2. Are we funding a real customer need?

The voice that should drive investment decisions is often the one that is not in the room: your customer.

One of my top priorities is making sure we have incredible feet on the street, working with customers to understand their pain points and challenges so we can help them innovate and create value.

One example: we heard from many enterprise customers who were struggling with fragmented AI efforts across their organization. Multiple initiatives were running in parallel with no central visibility or governance. That feedback led directly to an investment in developing what we call the AI Control Tower, a central hub for managing AI across the enterprise.

Some of the most expensive investment mistakes happen when there is a disconnect between what customers need and the products or innovations a company chooses to invest in. If you cannot trace a direct line from a customer insight to a major investment decision, that is a red flag.

3. Are customers adopting what we built and getting measurable business value from it?

An investment decision does not end once an initiative is greenlit — or even when sales are made and customers are onboarded. You have to care about whether customers are actually using what you built, and if it is embedded deeply in their operations and delivering real value.

I believe the teams closest to customers post-sale are often the best early-warning systems in the business. They see friction first and hear where adoption is stalling, or workflows are breaking down.

This is even more critical in this moment of AI adoption, where we know the real challenge lies in execution. According to ServiceNow’s own Enterprise AI Maturity Index, 59% of organizations are using agentic AI, but only 9% have made significant progress in creating autonomous, multistep AI workflows. That means companies are paying for capabilities they haven’t yet unlocked, so they’re not seeing the value they’re hoping for.

Of course, when your customers don’t see value, you’re inviting churn. At enterprise scale, even a single point of revenue retention can be worth hundreds of millions of dollars. This is money that should be driving investments in the right innovations and funding projects that create a competitive edge. Instead, it simply vanishes from the balance sheet.

Balancing bold bets with discipline

In my career, I have led through periods of real pressure. But the pressure companies feel right now to move quickly on AI is at a whole new level. That means companies must stay agile without becoming reactive. As I often tell my team, AI is creating incredible opportunities, but opportunities without prioritization are just noise.

These decisions are also never made in a vacuum. The key to success lies in making sure they are aligned across the business, grounded in what customers actually need, and tied to real value creation, not just experimentation. This is where discipline matters most: when something is not working, you have to be willing to close off the spigot and reallocate capital toward what is.

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